Agricultural Meteorology
S. Mirshekari; F. Yaghoubi; S.A. Hashemi
Abstract
Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the ...
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Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the countries in the Middle East with a different climate in each region of the country, has suffered significant adverse effects of climate change. Considering the importance of the climate change, it is important to investigate the changes in climate variables to know the future conditions and make management decisions. In the field of climate research, global climate models are useful tools that are often used to investigate the global climate system, including historical and projected periods. Since the use of the CMIP6 dataset provides improved clarity and accuracy for predicting future climate forecasts, the main objective of the present study is to predict the temperature and precipitation changes in the near, mid, and far future in Sistan-va-Baluchestan province.
Materials and Methods
The minimum temperature, maximum temperature, and precipitation data of 10 general circulation models (GCMs) of the 6th IPCC report for the baseline (1990-2014) were downloaded from the Global Climate Research Program database (https://esgf-node.llnl.gov). Then GCMs were including ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1-HR, CNRM-ESM2-1, EC-Earth3-CC, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MIROC6, and NorESM2-MM. Four statistical indicators including correlation coefficient (R2), RMSE, Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) were used to evaluate the performance of 10 GCMs. Based on the results obtained from the these indicators, the models that had higher performance in predicting the temperature and precipitation data were selected as the best models for forecasting in the future. The ensemble of these models under two SSP2-4.5 and SSP5-8.5 scenarios for the near, middle, and far future (2026-2050, 2051-2075, and 2076-2100) were extracted from the World Climate Research Program database.
CMhyd (Climate Model data for hydrologic modeling) tool was used to bias correction climate data of the selected models. In order to choose the best bias correction method, the R2, RMSE, NSE, and MAE were estimated.
After bias correction, the climate data of selected models were ensembled and then the changes in precipitation and maximum and minimum temperature in three future periods compared to the baseline was estimated.
Results and Discussion
The results showed that out of 10 GCMs, seven models had good performance (R2 > 0.40, 4.23 < RMSE < 12.02°C, 0.12 < NSE < 0.74, and 3.36 < MAE < 9.59°C) in simulating daily minimum and maximum temperature. However, the performance of all models in simulated daily precipitation was poor (R2 > 0.19, 1.24 < RMSE < 3.70 mm, -7.41 < NSE < -0.57, and 0.23 < MAE < 0.85 mm).
Among the different bias correction methods of temperature and precipitation available in CMhyd, the distribution mapping method had the best performance.
In all three regions, compared to the baseline, the average annual minimum and maximum temperature under two scenarios will increase in the future periods and precipitation will decrease in most periods and scenarios. These changes will be mainly in the SSP5-8.5 scenario compared to SSP2-4.5 and also in the far future period compared to the middle and near future. Averaged across all locations, annual maximum temperature showed increases in near, middle, and far projected periods of 1.3, 2.1, and 2.8°C under SSP2-4.5 and 1.6, 3.1, and 5.1°C under SSP5-8.5, respectively (Fig. 2), while for minimum temperature, the increases will be of 1.6, 2.6, and 3.4°C for SSP2-4.5 and 1.9, 3.9, and 6.3°C for SSP5-8.5. The range of annual precipitation among all sites was from –58.22 to 49.33% under SSP5-8.5 in the near and far future periods in Zabol and Iranshahr, respectively.
The annual increase in the average maximum and minimum temperature will be mainly due to the increase in air temperature in the months of January, February, August, September, October, November and December. The annual decrease in precipitation will mainly result from the decrease in precipitation in January, February, March, November, and December, and the annual increase in precipitation will result from the significant increase in precipitation in May and October compared to the baseline.
Conclusion
The results showed that under different scenarios of climate change, the maximum and minimum temperatures in the near, middle, and far future periods will face an increase compared to the baseline. However, the precipitation changes in the future time periods are not the same as compared to the baseline, and in some periods the precipitation will decrease and in others it will increase. But in general, the decrease in precipitation will be more than its increase. Therefore, it is very important to formulate and implement appropriate management programs for the needs of each region, in order to properly manage water resources and adapt to extreme temperatures and their consequences.
S. Kouzegaran; M. Mousavi Baygi; iman babaeian
Abstract
Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, ...
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Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, the study of affectability and pattern change of extreme atmospheric events is also important. Changes in climatic elements especially extreme temperature factors have a significant influence on the performance of farming systems. Accordingly, understanding changes in temperature parameters and extreme temperature indices is the prerequisite to sustainable development in agriculture and should be considered in management processes. Investigation of extreme values for planning and policy for the agricultural sector, water resource, environment, industry, and economic management is important. Materials and Methods: To evaluate the extreme temperature indices trend, some indices of temperature, recommended by the CCl/CLIVAR Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI), were considered using Rclimdex software. In this study, daily minimum and maximum temperature data retrieved from MPI-ESM-LR global climate model were used to predict future climate extreme events over the next three periods of 2026-2050, 2051-2075, and 2076-2100 based on IPCC scenarios of RCP4.5 and RCP8.5 of the studied area, covering South Khorasan province and southern part of Razavi Khorasan province, located in the east of Iran. The modified BCSD method was used to downscale extreme temperature data. Results and Discussion: Results showed an increasing trend of warm climate extreme. According to the output of Rclimdex for RCP4.5 scenario in the period of 2026-2050, it was observed that SU25 index, summer days, has a positive trend at all studied stations. This index was found to be significant and increased at all stations in the mid-term future period, and it had an increasing trend in the far future period, which was not significant. The number of Tropical Nights (TR20) index had a positive trend at all. In the mid-term future period, there was a significant increasing trend for some stations, while there were some negative and insignificant trends at some stations in the far future. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations, and the mid-term future period had a significant increasing trend, while the trend was decreasing in the far future period. Results for temperature extreme indices under RCP8.5 scenario showed that SU25 index had a positive trend at all stations studied in the near future, mid-term, and far future period. Index of tropical nights (TR20) had an upward trend, which was significant in mid-term and far future periods at most stations. Percentage of days in which maximum temperature is below than 10th percentile (TX10P), indicating a decrease in cold days, had a negative trend for all stations in the near future period. In the mid-term and far future periods, this trend was significant at all stations. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations and all three periods, and the trend was significant in the mid-term future. Conclusion: Minimum and maximum daily temperatures of MPI-ESM-LR global climate model were used to predict climatic extreme events during three future periods of 2026-2050, 2051-2075, and 2076-2100 under RCP4.5 and RCP8.5 scenarios at some stations located in South Khorasan province and southern part of Khorasan Razavi province. During the three studied future periods, extreme temperature indices changed significantly. The results showed that in both periods over the future years under the both scenarios, hot extreme indices would increase and cold extreme indices would decrease. It was observed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights increased, while cold extreme indices had a decreasing trend in the period of study, which shows a decrease in the severity and frequency of cold events.
B. Mirkamandar; Seied Hosein Sanaei-Nejad; H. Rezaee-Pazhand; M. Farzandi
Abstract
Introduction: The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients ...
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Introduction: The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients could be used only as approximation for estimating daily average temperature. Climatic conditions of the meteorological stations were also another parameter to be considered. This research presents a new method for estimating daily average of temperature in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately. Materials and Methods: E. Eccel (2012) developed algorithms to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation and relative humidity from available daily data. During the day, air temperature was calculated by: (1) (2) where S (t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: Current air Temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea). Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating daily average temperature in arid and semiarid regions of Iran. Final pattern has interception and new coefficients for estimate daily average of temperature. (3) Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled with linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters used in the present work. First a classification which provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation and three indices of De Martonne, Ivanov and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range and arid and semi-arid zone. Several clustering methods were used and around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation testes. Finally clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating daily average of temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis. Results and Discussion: The coefficient of determination (R2) and the Fisher statistics show that the patterns have a good ability to estimate the daily average of temperature. The daily average temperature pattern confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The average values of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. The mean squared error is a measure of the applicability of patterns. The accuracy of the estimating daily average temperature by the recommended models in three climates was confirmed by calculating the mean squared error. The proposed patterns of this study had less error than common patterns. Thus, the patterns have a good ability to estimate daily average temperature. Conclusion: The maximum temperature in calculating daily average of temperature is more effective than the minimum temperature. The standardized coefficient (Beta) of the daily average temperature patterns in coastal cluster was 48.2% for the minimum temperature and 51.8% for the maximum temperature. The largest influence of the maximum temperature was 63.1% in mountainous cluster for estimating daily average temperature. Range of the interception in the equations was from -1.735 to 0.26. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The average of the residuals in each patterns was zero. According to the graphs stabilization of variance can be seen based on the residual on the each pattern in each cluster. The proposed patterns were calculated according to mathematical principles but the common patterns did not consider these mathematical principles. The mean squared error (MSE) of the proposed patterns are less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns. The largest difference between the proposed patterns and the common patterns for estimate the daily average of temperature was 24% in mountainous cluster. Climatic clustering was done for states. The monthly and annual average temperature can be reliably estimated by using the data of sample stations in each state. These findings can be used to estimate daily, monthly and annual average of relative humidity in three climates and sample stations. In addition, one can employ the method for estimating daily, monthly and annual average of relative humidity and temperature based on around climatological clustering of Iran and other stations. Annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation can also be applied to estimate daily, monthly and annual average of temperature and relative humidity more accurately.
shideh shams; Mohammad Mousavi baygi
Abstract
Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature ...
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Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature has raised 0.4 to 0.8⁰C. Instrumental records show that, with the exception of 1998, the 10 warmest year (during the last 150 years), occurred since 2000, and 2014 was the warmest year. Investigation of maximum and minimum air temperature temporal trend indicates that these two parameters behave differently over time. It has been shown that the minimum air temperature raises noticeably more than the maximum air temperature, which causes a reduction in the difference of maximum and minimum daily air temperature (daily temperature range, DTR). There are several factors that have an influence on reducing DTR such as: Urban development, farms’ irrigation and desertification. It has been shown that DTR reduction occurs mostly during winter and is less frequent during summer, which shows the season’s effect on the temperature trend. Considering the significant effects of the climatological factors on economic and agricultural management issues, the aim of this study is to investigate daily air temperature range for yearly, seasonal and monthly time scales, using available statistical methods.
Materials and Methods: Daily maximum and minimum air temperature records (from 1950 to 2010) were obtained from Mashhad Meteorological Organization. In order to control the quality of daily Tmax and Tmin data, four different types of quality controls were applied. First of all, gross errors were checked. In this step maximum and minimum air temperature data exceeding unlikely air temperature values, were eliminated from data series. Second, data tolerance was checked by searching for periods longer than a certain number of consecutive days with exactly the same temperatures. Third, a revision of internal consistence was done, verifying that daily Tmax always exceeds daily Tmin. Fourth, the temporal coherency was tested by checking if consecutive temperature records differ by more than 8 degrees. The homogeneity of the series was tested by means of the Standard Normal Homogeneity test, the Buishand range and the Pettitt tests, on yearly, seasonal and monthly time scales. Breakpoint can be detected by means of these methods. In addition, Von Neumann ratio test was used to explore the series’ randomness. Having investigated data’s randomness in this study, series’ trend was determined by the Kendal-Tau test. Furthermore, the slope of the series’ trend was calculated using the Sen’s slope method.
Results Discussion: Results indicated a decreasing trend in DTR during last 60 years (1951-2010) in Mashhad climatological station. Moreover, the results revealed that the slope of yearly DTR was decreasing (-0.029 ⁰C per year), which indicates that minimum air temperature values raise more maximum air temperature values. A breakpoint was detected during 1985. During 1951-1985, the average amount of DTR was 14.6⁰C, while this parameter reduced to 12.9⁰C for the period 1985-2010. The Kendall-Tau test was used to obtain the significance of trend during 1951-2010, 1951-1985 and 1985-2010. The results showed that during 1951-2010, DTR significantly reduced at a rate of 0.29oC per decade. However, between 1951 and 1985, DTR trend increased at a rate of 0.61oC per decade, while DTR trend between 1985 and 2010 reduced at a rate of 0.19 ⁰C per decade, which was not significant (P-value=5%). In the seasonal DTR series, the highest trend’s slope was calculated for the summer data (-0.43 ⁰C in a decade), while the lowest one accrued in spring (-0.15⁰C in a decade). From 1951 to 1985, DTR had an increasing trend, due to minimum air temperature’s downward trend. But from the late 1980 to 2010, as it was expected, downward DTR trend was observed, because during this period minimum air temperature increases more than the maximum air temperature, thus the difference between Tmax and Tmin was reduced. Monthly DTR analysis also revealed a decreasing trend from 1951 to 2010, except for March and April, which had a non-significant increasing trend. In monthly DTR series, as it was expected, similar to the yearly and seasonal time series, the breakpoints accrued around 1985 in 8 out of 12 months. During February, March, April and November no significant breakpoint was detected.
Conclusion: DTR decreasing trend indicated that minimum air temperature increase was greater than maximum. This can cause a significant effect on the agricultural sector, hence in an appropriate agricultural management, these points should be considered. For example, changing the sowing time is one of the decisions which a manager can make.
Sh. Shams; Mohammad Mousavi baygi
Abstract
Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly ...
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Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly and daily, was investigated by means of SNHT, Buishand, Pettitt, Von-neumann and kendall-tau. The results of this study indicate a temperature increase of Mashhad, comparison of the results showed that during the past 60 years (1951-2010), minimum temperature increased 2times more than maximum temperature (0.062 versus 0.031). Test results also showed temperature increasing in all seasons, but just winter maximum temperature increasing trend was not significant in 95% confidence level. Also the highest rate of temperature increasing was belonged to autumn minimum temperature, with the slope of 0.074. Like the difference between annual series, in all season minimum temperature increasing trend is higher than maximum trend, comparing trends in monthly maximum and minimum temperatures show similar results. It also was shown that the minimum temperature trend rose approximately near the year 1985, while maximum temperature break point is near 1995.
